Generalizing resemblance coefficients to accommodate incomplete data

Large ecological data matrices may be incomplete for various reasons, preventing the use of standard multidi-mensional scaling (ordination) and cluster analysis packages. Although there exist a few resemblance functions that allow missing scores, there is no theoretical background and software support for most distance and simi-larity coefficients potentially applied in multivariate data analysis. We provide a general framework for a precise mathematical redefinition of a large set of resemblance functions originally developed for complete data sets with presence-absence (binary) or ratio-scale variables. Included are coefficients which consider double absences in abundance data. Potential problems with the use of these functions are discussed, with the conclusion that incompleteness of data would rarely if ever influence greatly the interpretability of ordinations and classifica-tions. An R function described in the Appendix represents a link to R. We also provide a stand-alone WINDOWS application for users of other computer programs. The new software will allow users of standard data analysis packages to perform multivariate analysis using a wide variety of resemblance coefficients even if the data are incomplete for whatever reason

Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review

Context The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with over 6000 software projects. This dataset makes it possible to estimate a project s size, effort, duration, and cost. Objective The aim of this study was to determine how and to what extent, ISBSG has been used by researchers from 2000, when the first papers were published, until June of 2012. Method A systematic mapping review was used as the research method, which was applied to over 129 papers obtained after the filtering process. Results The papers were published in 19 journals and 40 conferences. Thirty-five percent of the papers published between years 2000 and 2011 have received at least one citation in journals and only five papers have received six or more citations. Effort variable is the focus of 70.5% of the papers, 22.5% center their research in a variable different from effort and 7% do not consider any target variable. Additionally, in as many as 70.5% of papers, effort estimation is the research topic, followed by dataset properties (36.4%). The more frequent methods are Regression (61.2%), Machine Learning (35.7%), and Estimation by Analogy (22.5%). ISBSG is used as the only support in 55% of the papers while the remaining papers use complementary datasets. The ISBSG release 10 is used most frequently with 32 references. Finally, some benefits and drawbacks of the usage of ISBSG have been highlighted. Conclusion This work presents a snapshot of the existing usage of ISBSG in software development research. ISBSG offers a wealth of information regarding practices from a wide range of organizations, applications, and development types, which constitutes its main potential. However, a data preparation process is required before any analysis. Lastly, the potential of ISBSG to develop new research is also outlined.Fernández Diego, M.; González-Ladrón-De-Guevara, F. (2014). Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review. Information and Software Technology. 56(6):527-544. doi:10.1016/j.infsof.2014.01.003S52754456

The usage of ISBSG data fields in software effort estimation: A systematic mapping study

[EN] The International Software Benchmarking Standards Group (ISBSG) maintains a repository of data about completed software projects. A common use of the ISBSG dataset is to investigate models to estimate a software project's size, effort, duration, and cost. The aim of this paper is to determine which and to what extent variables in the ISBSG dataset have been used in software engineering to build effort estimation models. For that purpose a systematic mapping study was applied to 107 research papers, obtained after a filtering process, that were published from 2000 until the end of 2013, and which listed the independent variables used in the effort estimation models. The usage of ISBSG variables for filtering, as dependent variables, and as independent variables is described. The 20 variables (out of 71) mostly used as independent variables for effort estimation are identified and analysed in detail, with reference to the papers and types of estimation methods that used them. We propose guidelines that can help researchers make informed decisions about which ISBSG variables to select for their effort estimation models.González-Ladrón-De-Guevara, F.; Fernández-Diego, M.; Lokan, C. (2016). The usage of ISBSG data fields in software effort estimation: A systematic mapping study. Journal of Systems and Software. 113:188-215. doi:10.1016/j.jss.2015.11.040S18821511

a comparative study of absent features and unobserved values in software effort data

Software effort data contains a large amount of missing values of project attributes. The problem of absent features, which occurred recently in machine learning, is often neglected by researchers of software engineering when handling the missingness in software effort data. In essence, absent features (structural missingness) and unobserved values (unstructured missingness) are different cases of missingness although their appearance in the data set are the same. This paper attempts to clarify the root cause of missingness of software effort data. When regarding missingness as absent features, we develop Max-margin regression to predict real effort of software projects. When regarding missingness as unobserved values, we use existing imputation techniques to impute missing values. Then, Ε -SVR is used to predict real effort of software projects with the input data sets. Experiments on ISBSG (International Software Benchmarking Standard Group) and CSBSG (Chinese Software Benchmarking Standard Group) data sets demonstrate that, with the tasks of effort prediction, the treatment regarding missingness in software effort data set as unobserved values can produce more desirable performance than that of regarding missingness as absent features. This paper is the first to introduce the concept of absent features to deal with missingness of software effort data. © 2012 World Scientific Publishing Company.National Natural Science Foundation of China 60903050, 71101138; Beijing Natural Science Fund 4122087; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education MinistrySoftware effort data contains a large amount of missing values of project attributes. The problem of absent features, which occurred recently in machine learning, is often neglected by researchers of software engineering when handling the missingness in software effort data. In essence, absent features (structural missingness) and unobserved values (unstructured missingness) are different cases of missingness although their appearance in the data set are the same. This paper attempts to clarify the root cause of missingness of software effort data. When regarding missingness as absent features, we develop Max-margin regression to predict real effort of software projects. When regarding missingness as unobserved values, we use existing imputation techniques to impute missing values. Then, Ε -SVR is used to predict real effort of software projects with the input data sets. Experiments on ISBSG (International Software Benchmarking Standard Group) and CSBSG (Chinese Software Benchmarking Standard Group) data sets demonstrate that, with the tasks of effort prediction, the treatment regarding missingness in software effort data set as unobserved values can produce more desirable performance than that of regarding missingness as absent features. This paper is the first to introduce the concept of absent features to deal with missingness of software effort data. © 2012 World Scientific Publishing Company